In low-latitude areas less than 10° in latitude angle, the solar radiation that goes into the solar still increases as the cover slope approaches the latitude angle. However, the amount of water that is condensed and then falls toward the solar-still basin is also increased in this case. Consequently, the solar yield still is significantly decreased, and the accuracy of the prediction method is affected. This reduction in the yield and the accuracy of the prediction method is inversely proportional to the time in which the condensed water stays on the inner side of the condensing cover without collection because more drops will fall down into the basin of the solar-still. Different numbers of scraper motions per hour (NSM), that is, 1, 2, 3, 4, 5, 6, and 7, are implemented to increase the hourly yield of solar still (HYSS) of the double-slope solar still hybrid with rubber scrapers (DSSSHS) in areas at low latitudes and develop an accurate model for forecasting the HYSS. The proposed m... [more]

Permanent magnet synchronous machines (PMSMs) with an overhang structure can increase power density by compensating for the increased magnetic energy of permanent magnets. To analyze the overhang structure, a three-dimensional (3D) analysis of PMSMs is essential. However, 3D analysis takes a long time and the modeling process is complicated in the initial design stage. To overcome these problems, a magnetic equivalent circuit technique is applied to the 2D model. In this paper, an optimal design method for PMSMs with an overhang structure is proposed based on the semi 3D magnetic equivalent circuit (MEC) and random walk method. By using semi 3D MEC, it is possible to quickly analyze PMSM and obtain accurate electromagnetic analysis results considering the overhang effect. Moreover, the volume and weight of PMSM can be minimized by optimizing the rotor’s four design parameters using a random walk algorithm. To obtain high efficiency, the objective function is selected so that copper los... [more]

Regional integrated energy systems (IESs) have emerged to satisfy the increasing diversified energy demand in Tibet. However, limited resource allocation of a given IES can occur because of the uncertainty in the output and prediction error of distributed renewable energy (DRE). A distributionally robust optimization (DRO) model was proposed for the joint operation of multiple regional IESs, and multi-energy sharing and multi-energy flow coupling of electricity, heat, and oxygen were considered. The probability distribution of the DRE output was described using 1− norm and ∞− norm constraints, and the minimum operating cost under adverse scenarios was determined through DRO. Furthermore, on the premise of ensuring cluster profit, a pricing mechanism of the energy supply within the cluster was proposed. Finally, a typical model involving eight cases was established and analyzed. The results revealed that multi-energy sharing and multi-energy flow coupling improved the economy of IES clu... [more]

In this paper, we study the problem of managing an energy community hosting a fleet of electric vehicles for rent. On the day ahead, service requests for electric vehicles are submitted to the community. Then, the optimal request-to-vehicle assignment has to be found, as well as the optimal charging schedule of vehicle batteries. A suitable model is presented and included in an existing energy community architecture. The overall community management problem is formulated as a bi-level model, featuring two nested optimization problems. The optimal request-to-vehicle assignment requires the solution of a mixed-integer linear program. To reduce the computational complexity, a heuristic solution to the assignment problem is presented. Numerical results show that participation in the community grants a remarkable reduction in the electric vehicle charging cost. The adoption of the heuristic assignment solution provides a dramatic reduction in the computation time required to solve the bi-le... [more]

Electricity markets are going through a comprehensive transformation that includes the large-scale appearance of intermittent renewable generators (RGs). To handle the local effects of new RGs on the distribution grid, the more efficient utilization of distributed local flexibility (LF) resources is necessary. However, the optimal market design is not yet known for LF products. This paper investigates a novel cost allocation mechanism in the context of this market challenge. The mechanism is designed to provide several important advantages of peer-to-peer trading without creating barriers to practical application. It provides partial disintermediation. The acquisition of LF remains the responsibility of the DSO, while the financial costs of the transaction are covered on power exchanges (PXs). To provide this functionality, the clearing algorithm of the PX in question has to incorporate a novel feature we call the Payment Redistribution Technique. This technique allows the buyers’ expe... [more]

This paper investigates and compares several highly efficient thermodynamic cycles that are suitable for coupling with particle-in-tube fluidized-bed solar receiver technology. In such a receiver, high-temperature particles are used as both a heat transfer fluid and a storage medium. A dense particle suspension (DPS) is created through an upward bubbling fluidized-bed (UBFB) flow inside the receiver tubes, which constitutes the “particle-in-tube” solar receiver concept. Reaching higher temperatures is seen as a key factor for future cost reductions in the solar plant, as this leads to both higher power conversion efficiency and increased energy storage density. Three advanced thermodynamic cycles are analyzed in this work: the supercritical steam Rankine cycle (s-steam), supercritical carbon dioxide cycle (s-CO2) and integrated solar combined cycle (ISCC). For each one, 100% solar contribution, which is considered the total thermal input to the power cycle, can be satisfied by the sola... [more]

As the installation of solar-photovoltaic and wind-generation systems continue to grow, the location must be strategically selected to maintain a reliable grid. However, such strategies are commonly subject to system adequacy constraints, while system security constraints (e.g., frequency stability, voltage limits) are vaguely explored. This may lead to inaccuracies in the optimal placement of the renewables, and thus maximum benefits may not be achieved. In this context, this paper proposes an optimization-based mathematical framework to design a robust distributed generation system, able to keep system stability in a desired range under system perturbance. The optimum placement of wind and solar renewable energies that minimizes the impact on system stability in terms of the standard frequency deviation is obtained through particle swarm optimization, which is developed in Python and executed in PowerFactory-DIgSILENT. The results reveal that the proposed approach has the potential t... [more]

Spacers for the HVDC GIS/GIL play an important role in mechanically supporting conductors and separating compartments. At the same time, their insulation performance affects the stability and safety of system operation. Design rules and knowledge specific to AC spacers do not apply to those of DC spacers. Considering the shape influence on the surface electric field intensity of the spacer under HVDC applied voltage, as determined in our previous work, an optimized shape of a spacer model based on finite element electric field calculations and using standard HVAC alumina filled epoxy material and two novel types of materials were studied. The simulation’s results show that the DC shape optimization of the spacers can effectively reduce the electric field magnitudes along the spacer under different temperature gradients. To verify practically these findings, this paper presents the reduced scale gas insulated prototype that was constructed, the optimized DC spacers that were fabricated... [more]

The present work consists of developing a new hybrid FL-INC optimization algorithm for the solar water pumping system (SWPS) through a SEPIC converter whose objective is to improve these performances. This technique is based on the combination of the fuzzy logic of artificial intelligence and the incremental conductance (INC) technique. Indeed, the introduction of fuzzy logic to the INC algorithm allows the extraction of a maximum amount of power and an improvement in the efficiency of the SWPS. The performance of the system through the SEPIC converter is compared with those of the direct coupling to show the interest of the indirect coupling, which requires an adaptation stage driven by an optimal control algorithm. In addition, a comparative analysis between the proposed hybrid algorithm and the conventional optimization techniques, namely, P&O and INC Modified (M-INC), was carried out to confirm improvements related to the SWPS in terms of efficiency, tracking speed, power quality,... [more]

A recloser requires a fast operating time in the first shot to optimally clear a temporary fault. The operating time is dependent on the time-dial, the pick-up settings, and the fault current. The recloser detects the fault current from the grid supply; however, the connection of the generators in the distribution system can contribute to the fault current. Depending on the location of the generators and the direction of the current, the fault current can decrease and cause an increase in the operating time. Therefore, the optimal settings that can minimize the operating time may need to be determined. This paper simulates the behavior of a recloser in its first shot for clearing a temporary fault and tests its performance in an active distribution system that has two types of distributed generators. It then uses the differential evolution algorithm to find the optimal settings in the active distribution voltage conditions. It also applies modifications to the differential evolution al... [more]

The mine return air flow has the characteristics of basically constant temperature and humidity all year round and is a high-quality waste heat resource. Its direct discharge not only wastes energy but also causes environment pollution. It has important economic value and application prospect to solve the problem of shaft antifreeze using new technology to recover the waste heat of mine return air. Gravity heat pipe is widely used in the heat recovery of mine return air. Its heat transfer process is a complex process with multiple parameters. The current research focuses on the influence of a single factor on heat transfer, which has many limitations. To analyze the effects of different parameters on the heat recovery effect of gravity heat pipe in mine return air and to optimize heat pipe heat exchanger parameters in the heat exchange system, mathematical models of gas−water countercurrent heat and mass transfer, entransy dissipation and exergy efficiency were established in this pape... [more]

Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables’ penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrated local energy community (ILEC), where active consumers make common choices for satisfying their energy needs through the optimal management of a set of multi-carrier energy technologies, by achieving better economic and environmental benefits compared to the business-as-usual scenario. This paper discusses the stochastic operation optimization of the smart Savona Campus of the University of Genoa, according to economic and environmental criteria. The campus is treated as an ILEC with two electrically interconnected multi-energy hubs involving technologies such as PV, solar thermal, combined heat and power systems, electric and geothermal hea... [more]

The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polyn... [more]

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, such as artificial neural networks, are the most covered in the literature review. All these approaches evaluate the prediction error when comparing the generated models with the data that fed the model, but they do not compare these values with the actual data of electricity demand once these are obtained, in addition, these techniques present high error values when there are unexpected changes in the trend of electricity consumption. This work proposes a methodology to generate an adaptive model for unexpected changes in electricity demand through the use of optimization in conjunction with SARIMA time series. The proposed case study is the electricity consumption in Quito, Ecuador to predict... [more]

The design of new dual-purpose thermal desalination plants is a combinatory problem because the optimal process configuration strongly depends on the desired targets of electricity and freshwater. This paper proposes a mathematical model for selecting the optimal structure, the operating conditions, and sizes of all system components of dual-purpose thermal desalination plants. Electricity is supposed to be generated by a combined-cycle heat and power plant (CCHPP) with the following candidate structures: (a) one or two gas turbines; (b) one or two additional burners in the heat recovery steam generator; (c) the presence or missing a medium-pressure steam turbine; (d) steam generation and reheating at low pressure. Freshwater is supposed to be obtained from two candidate thermal processes: and (e) a multi-effect distillation (MED) or a multi-stage flash (MSF) system. The number of effects in MED and stages in MSF are also discrete decisions. Different case studies are presented to show... [more]

Pre-acid fracturing is an effective technique to improve productivity of tight reservoirs. While acid injection can clean the formation and improve the fracturing performance by reducing the fracture pressure of the reservoir, the chemical reaction of the acid solution with proppant may reduce the compressive strength of the proppant and therefore negatively affect the fracture conductivity. In this study, we experimentally investigated the solubility of the proppant in acid and the effect of acid corrosion on proppant compressive strength and fracture conductivity. The results show that the concentration of the acid solution has the greatest effect on solubility of the proppant, which is followed by the contact reaction time. Though a proppant of larger particle size indicates a lower solubility, the acid corrosion poses a greater damage to its compressive strength and conductivity. The quartz sand proppant exhibits superior stability to ceramic proppant when they are subjected to aci... [more]

In this study, a new meta-heuristic optimization method inspired by the behavioral choices of animals and hunger-driven activities, called hunger games search (HGS), is suggested to solve and formulate the single- and multi-objective optimal power flow problem in power systems. The main aim of this study is to optimize the objective functions, which are total fuel cost of generator, active power losses in transmission lines, total emission issued by fossil-fueled thermal units, voltage deviation at PQ bus, and voltage stability index. The proposed HGS approach is optimal and easy, avoids stagnation in local optima, and can solve multi-constrained objectives. Various single-and multi-objective (conflicting) functions were proposed simultaneously to solve OPF problems. The proposed algorithm (HGS) was developed to solve the multi-objective function, called the multi-objective hunger game search (MOHGS), by incorporating the proposed optimization (HGS) with Pareto optimization. The fuzzy... [more]

A louver is a traditional environmental control device and passive architectural element based on an ecofriendly concept. Louvers are architectural elements that can be used to regulate natural lighting, thermal environment, and building energy use. To realize these integrated functionalities of louvers, they must be designed considering the climate and geographical characteristics of the target region. However, these aspects are typically not considered during building design in Korea, resulting in lovers being used as design elements with simple natural lighting control functions. Therefore, the objective of this study was to promote the integrated use of louvers by optimizing the louver angle according to the microclimate in Korea from the viewpoint of thermal energy use. We performed load and energy simulation planning and calculation and conducted optimization studies for the louver angle and range of motion for each region. The energy consumption in central and southern Korean re... [more]

Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is establis... [more]

Heating and cooling systems account for a considerable portion of the energy consumed for domestic reasons in Europe. Burning fossil fuels is the main way to produce this energy, which has a detrimental effect on the environment. It is essential to consider a building’s characteristics when determining how much heating and cooling is necessary. As a result, a study of the related buildings’ characteristics, such as the type of cooling and heating systems required for maintaining appropriate indoor air conditions, can help in the design and construction of energy-efficient buildings. Numerous studies have used machine learning to predict cooling and heating systems based on variables that include relative compactness, orientation, overall height, roof area, wall area, surface area, glazing area, and glazing area distribution. Fuzzy logic, however, is not used in any of these methods. In this article, we study a fuzzy logic approach, i.e., HHO−ANFIS (combination of Harris hawks optimizat... [more]

Microalgae have attracted great research interest as a feedstock for producing a wide range of end-products. However, recent studies show that the tight processing integration technology for microalgae-based biorefinery makes production less economical and even has a negative impact on sustainability. In this study, a new two-tier superstructure optimization design methodology is proposed to locate the optimal processing pathway. This model is developed based on the decomposition strategy and the relationship-based investigation, coupling an outer-tier structure with an inner-tier structure, wherein the outlet flows of the middle stages is relaxed and then an appropriate level of redundancy for designing the processing is provided. Two scenarios are developed to compare the most promising biorefinery configurations under two different design option favors. By solving the mixed integer nonlinear programming model with the objective functions of maximizing the yield of the desired produc... [more]

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.

Aiming at the problem of wind turbine generator fault early warning, a wind turbine fault early warning method based on nonlinear decreasing inertia weight and exponential change learning factor particle swarm optimization is proposed to optimize the deep belief network (DBN). With the data of wind farm supervisory control and data acquisition (SCADA) as input, the weights and biases of the network are pre-trained layer by layer. Then the BP neural network is used to fine-tune the parameters of the whole network. The improved particle swarm optimization algorithm (IPSO) is used to determine the number of neurons in the hidden layer of the model, pre-training learning rate, reverse fine-tuning learning rate, pre-training times and reverse fine-tuning training times and other parameters, and the DBN predictive regression model is established. The experimental results show that the proposed model has better performance in accuracy, training time and nonlinear fitting ability than the DBN... [more]

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load... [more]

Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable f... [more]